Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470634
Sheryl Gupta, Parag Agarwal, M.S. Nidhya
The Assessing the Usefulness of Hyper Spectral Imaging for interpreting disease Pathways in Sustainable Clinical Environments undertaking examines the software of hyperspectral imaging (HSI) for determining the underlying pathologies of sicknesses. HSI is a novel imaging technique that acquires spectral records from one-of-a-kind bands of electromagnetic radiation, which presents increases in facts approximately the physical houses of natural materials. The undertaking strives to become aware of correlations between particular spectral traits and disease pathways by constructing spectral libraries and linking spectral functions to the ailment with contrast. The datasets created via the assignment can then assist in telling destiny medical selections and remedies. Additionally, this project will contribute to developing sustainably managed scientific environments and improving the health outcomes of patients. The research carried out by this venture pursues to provide perception into quantifying the agreements among spectral features and ailment pathways for each diagnosis and analysis.
{"title":"Assessing the Usefulness of Hyper Spectral Imaging for Decoding Disease Pathways in Sustainable Medical Environments","authors":"Sheryl Gupta, Parag Agarwal, M.S. Nidhya","doi":"10.1109/ICOCWC60930.2024.10470634","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470634","url":null,"abstract":"The Assessing the Usefulness of Hyper Spectral Imaging for interpreting disease Pathways in Sustainable Clinical Environments undertaking examines the software of hyperspectral imaging (HSI) for determining the underlying pathologies of sicknesses. HSI is a novel imaging technique that acquires spectral records from one-of-a-kind bands of electromagnetic radiation, which presents increases in facts approximately the physical houses of natural materials. The undertaking strives to become aware of correlations between particular spectral traits and disease pathways by constructing spectral libraries and linking spectral functions to the ailment with contrast. The datasets created via the assignment can then assist in telling destiny medical selections and remedies. Additionally, this project will contribute to developing sustainably managed scientific environments and improving the health outcomes of patients. The research carried out by this venture pursues to provide perception into quantifying the agreements among spectral features and ailment pathways for each diagnosis and analysis.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"23 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470627
Ashish Bishnoi, A. Kannagi, Kalyan Acharjya
Semi-supervised adversarial transfer gaining knowledge of (SATL) has been proposed as a powerful method for automatic pores and skin lesion segmentation. This approach aims to transfer knowledge from a categorized supply area to an unlabeled target domain to enhance the segmentation accuracy. The approach uses a generative opposed community (GAN) to study a function area that's then used to switch the segmentation knowledge from the source to the target domain. Experiments have proven that SATL can enhance segmentation accuracy in the target domain by using as few as 2000 supply domain annotations. Usual, SATL provides a powerful method for automatic pores and skin lesion segmentation in domain names with limited amounts of labeled information and will probably revolutionize medical imaging diagnostics.
{"title":"Semi-Supervised Adversarial Transfer Learning for Automated Skin Lesion Segmentation","authors":"Ashish Bishnoi, A. Kannagi, Kalyan Acharjya","doi":"10.1109/ICOCWC60930.2024.10470627","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470627","url":null,"abstract":"Semi-supervised adversarial transfer gaining knowledge of (SATL) has been proposed as a powerful method for automatic pores and skin lesion segmentation. This approach aims to transfer knowledge from a categorized supply area to an unlabeled target domain to enhance the segmentation accuracy. The approach uses a generative opposed community (GAN) to study a function area that's then used to switch the segmentation knowledge from the source to the target domain. Experiments have proven that SATL can enhance segmentation accuracy in the target domain by using as few as 2000 supply domain annotations. Usual, SATL provides a powerful method for automatic pores and skin lesion segmentation in domain names with limited amounts of labeled information and will probably revolutionize medical imaging diagnostics.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"34 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470745
Poonam Gupta, Ankit Varshney, K. Suneetha
this observation applies nonlinear dimensionality reduction methodologies to enhance the accuracy of target identity from hyper spectral facts. The point of interest is on three-dimensionality discount strategies, namely, nonlinear fundamental element analysis (NLPCA), nonlinear independent aspect analysis (NICA), and nonlinear projection (NP). The overall performance is evaluated on a publicly to be had Indian Civil Airborne Hyper spectral Experimental (INCAS) dataset. Consequences from this investigation demonstrate that the NLPCA set of rules gives stepped-forward overall performance compared to the two different techniques. It is also famous for noticeably low processing time and memory requirements and a validation accuracy of 93.3%. As a consequence, this look strengthens the argument that nonlinear methods are beneficial for evaluating hyper spectral records. The studies take a look at investigating the use of three nonlinear dimensionality discount techniques-Kernel impartial component evaluation (KICA), Kernel Non-negative Matrix Factorization (KNMF), and Elastic net independent issue evaluation (ENICA) to beautify target identification from hyper spectral records. Hyper spectral information is a powerful tool for classy goal identification because of its high-dimensional nature. However, excessive-dimensional hyper spectral facts are typically replete with noise and mistakes, so easy linear strategies aren't enough to acquire the desired accuracy from target identity applications. To this give up, this look explores the suitability of kernel zed nonlinear function extraction methods for enhancing target identification accuracy. Thru the assessment of synthesized records, it was found that the nonlinear methods, when used together, could gain higher accuracies than simple linear strategies. Moreover, the proposed kernels-based total techniques have also improved category accuracy in challenging situations, such as when noise is a gift within the statistics. Therefore, the results of this look advise that kernel zed nonlinear dimensionality discount strategies can extensively enhance accuracy while performing hyper spectral goal identification.
{"title":"Exploring Non-Linear Dimensionality Reduction Methodology for Enhanced Target Identification from Hyper Spectral Data","authors":"Poonam Gupta, Ankit Varshney, K. Suneetha","doi":"10.1109/ICOCWC60930.2024.10470745","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470745","url":null,"abstract":"this observation applies nonlinear dimensionality reduction methodologies to enhance the accuracy of target identity from hyper spectral facts. The point of interest is on three-dimensionality discount strategies, namely, nonlinear fundamental element analysis (NLPCA), nonlinear independent aspect analysis (NICA), and nonlinear projection (NP). The overall performance is evaluated on a publicly to be had Indian Civil Airborne Hyper spectral Experimental (INCAS) dataset. Consequences from this investigation demonstrate that the NLPCA set of rules gives stepped-forward overall performance compared to the two different techniques. It is also famous for noticeably low processing time and memory requirements and a validation accuracy of 93.3%. As a consequence, this look strengthens the argument that nonlinear methods are beneficial for evaluating hyper spectral records. The studies take a look at investigating the use of three nonlinear dimensionality discount techniques-Kernel impartial component evaluation (KICA), Kernel Non-negative Matrix Factorization (KNMF), and Elastic net independent issue evaluation (ENICA) to beautify target identification from hyper spectral records. Hyper spectral information is a powerful tool for classy goal identification because of its high-dimensional nature. However, excessive-dimensional hyper spectral facts are typically replete with noise and mistakes, so easy linear strategies aren't enough to acquire the desired accuracy from target identity applications. To this give up, this look explores the suitability of kernel zed nonlinear function extraction methods for enhancing target identification accuracy. Thru the assessment of synthesized records, it was found that the nonlinear methods, when used together, could gain higher accuracies than simple linear strategies. Moreover, the proposed kernels-based total techniques have also improved category accuracy in challenging situations, such as when noise is a gift within the statistics. Therefore, the results of this look advise that kernel zed nonlinear dimensionality discount strategies can extensively enhance accuracy while performing hyper spectral goal identification.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"14 4","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470486
Ankita Agarwal, M. Gour, Manivasagam
This paper describes growing and enforcing a novel laptop-aided design (CAD) paradigm for the format of Very-huge-Scale included (VLSI) circuits. This technique integrates the setup standards of sound judgment synthesis, circuit optimization, and elapsed time estimation techniques to provide clients with a unified framework for designing modern VLSI structures. The proposed paradigm consists of machine studying techniques, such as Gaussian techniques and convex programming, to optimize the selection of components and evaluate layout placements. The designed circuit is then expected via simulation, measurement, and evaluation. The proposed CAD paradigm is validated in an 8-bit ripple-convey adder system layout. The results of the take a look at provide robust proof that this novel CAD-primarily based paradigm is a powerful technique for designing modern VLSI systems.
{"title":"Establishing a Novel CAD-Based Paradigm for Design of VLSI Integrated Circuits","authors":"Ankita Agarwal, M. Gour, Manivasagam","doi":"10.1109/ICOCWC60930.2024.10470486","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470486","url":null,"abstract":"This paper describes growing and enforcing a novel laptop-aided design (CAD) paradigm for the format of Very-huge-Scale included (VLSI) circuits. This technique integrates the setup standards of sound judgment synthesis, circuit optimization, and elapsed time estimation techniques to provide clients with a unified framework for designing modern VLSI structures. The proposed paradigm consists of machine studying techniques, such as Gaussian techniques and convex programming, to optimize the selection of components and evaluate layout placements. The designed circuit is then expected via simulation, measurement, and evaluation. The proposed CAD paradigm is validated in an 8-bit ripple-convey adder system layout. The results of the take a look at provide robust proof that this novel CAD-primarily based paradigm is a powerful technique for designing modern VLSI systems.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"74 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470490
Amandeep Gill, Rahul Pawar, Ritesh Kumar
Hyperspectral photo processing (HIP) is an analytical method for recognizing and examining features in excessive-dimensional record sets. One of the demanding situations faced with the aid of HIP is the presence of noisy capabilities that may make it challenging to understand actual statistics and degrade the accuracy of the evaluation. A hybrid time series clustering technique has been proposed to symbolize and categorize noisy hyperspectral photos. This approach combines two different clustering algorithms (self-organizing map (SOM) and hierarchical clustering (HC)) with signal compressors (wavelet remodel (WT) and discrete cosine transform (DCT)) to come across and reduce noise. This approach has been proven to have better accuracy than traditional methods for hyperspectral photo processing. It also enables higher detection of features and offers a more accurate representation of the facts set, permitting researchers to higher hit upon subtle functions that conventional strategies may forget.
{"title":"Utilizing Hybrid Time Series Clustering Algorithms for Hyper Spectral Image Processing","authors":"Amandeep Gill, Rahul Pawar, Ritesh Kumar","doi":"10.1109/ICOCWC60930.2024.10470490","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470490","url":null,"abstract":"Hyperspectral photo processing (HIP) is an analytical method for recognizing and examining features in excessive-dimensional record sets. One of the demanding situations faced with the aid of HIP is the presence of noisy capabilities that may make it challenging to understand actual statistics and degrade the accuracy of the evaluation. A hybrid time series clustering technique has been proposed to symbolize and categorize noisy hyperspectral photos. This approach combines two different clustering algorithms (self-organizing map (SOM) and hierarchical clustering (HC)) with signal compressors (wavelet remodel (WT) and discrete cosine transform (DCT)) to come across and reduce noise. This approach has been proven to have better accuracy than traditional methods for hyperspectral photo processing. It also enables higher detection of features and offers a more accurate representation of the facts set, permitting researchers to higher hit upon subtle functions that conventional strategies may forget.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"44 15","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470803
C. Menaka, Nidhi Saraswat, Shri Bhagwan
Smooth mistakes in virtual circuits, which talk over with inadvertent modifications to saved bits or transmitted records because of temporary faults caused by external radiation, continue to be a trouble that ought to be tackled for the green functioning of virtual circuits. This technical abstract offers an overview of a research paper that examines the effectiveness of diverse gentle-blunders fee reduction algorithms in digital circuits. The research paper starts by introducing three techniques for mitigating tender error costs in virtual circuits: duplication, scrubbing, and blunders-correction codes. The paper then provides an evaluation of the behavior of several present smooth-mistakes charge reduction algorithms, including the Triple-creation Code, the DSP Compressor Code, the DFT feet Fault Codes, and the Alpha Detector-based Code. Through simulations and modeling, the paper evaluates the efficiency of those algorithms and compares their performances in opposition to each other and in opposition to baseline values. The paper reports that the Triple-creation Code, DSP Compressor Codes, and Alpha Detector-based total Code are able to present a powerful softerror charge reduction of up to 4 instances of the baseline values in digital circuits. There was also determined to be a sizable development in the robustness of virtual circuits when such algorithms are carried out.
{"title":"Exploring the Behavior of Soft-Error Rate Reduction Algorithms in Digital Circuits","authors":"C. Menaka, Nidhi Saraswat, Shri Bhagwan","doi":"10.1109/ICOCWC60930.2024.10470803","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470803","url":null,"abstract":"Smooth mistakes in virtual circuits, which talk over with inadvertent modifications to saved bits or transmitted records because of temporary faults caused by external radiation, continue to be a trouble that ought to be tackled for the green functioning of virtual circuits. This technical abstract offers an overview of a research paper that examines the effectiveness of diverse gentle-blunders fee reduction algorithms in digital circuits. The research paper starts by introducing three techniques for mitigating tender error costs in virtual circuits: duplication, scrubbing, and blunders-correction codes. The paper then provides an evaluation of the behavior of several present smooth-mistakes charge reduction algorithms, including the Triple-creation Code, the DSP Compressor Code, the DFT feet Fault Codes, and the Alpha Detector-based Code. Through simulations and modeling, the paper evaluates the efficiency of those algorithms and compares their performances in opposition to each other and in opposition to baseline values. The paper reports that the Triple-creation Code, DSP Compressor Codes, and Alpha Detector-based total Code are able to present a powerful softerror charge reduction of up to 4 instances of the baseline values in digital circuits. There was also determined to be a sizable development in the robustness of virtual circuits when such algorithms are carried out.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"44 27","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470557
A. Kannagi, Chetan Chaudhary, Jyoti Seth
Hyperspectral pictures are complicated information items with high spectral resolution, making their categorization and analysis timeingesting and challenging. Traditional strategies for classifying hyperspectral pix may be unreliable and gradually attributable to the presence of diverse noise resources and a high range of pixels. This paper proposes a new unsupervised classification approach for hyperspectral pictures using function extraction and fuzzy common sense. The method starts by first using feature extraction techniques on the hyperspectral pictures to lessen the dimensionality of the facts. Numerous characteristic extraction algorithms, including primary thing analysis (PCA) and impartial component evaluation (ICA), are tested to determine which function extraction algorithms yield satisfactory effects. The reduced function area is then used as an entry for the fuzzy category system. The bushy common sense device is used to classify the hyperspectral pix into distinctive classes according to the extracted capabilities. Experimental results display that the proposed method achieves proper effects for the category venture with classification accuracy accomplishing as high as 79%. The proposed technique demonstrates advanced performance over conventional category strategies in terms of each accuracy and speed. Hyperspectral pics (HSI) offer valuable statistics approximately the environment and the functions gift inside it. But, the sheer quantity of facts present in HSI makes guide evaluation of those photos a time-eating and exhausting project. As such, there is a growing demand for robust and reliable automated techniques to analyze HSI. In this context, unsupervised tactics for classifying HSI have gained interest due to their ability to examine facts without requiring manually categorized education facts. Fuzzy logic is one method being explored for unsupervised HSI type due to its capability to assign more than one label to pixels of the image and its robustness to noise. Right here, the HSI picture is first pre-processed and feature extracted to produce a fixed of numerical statistics that may be used to classify the pixels of the image extra as they should be. This feature extracted records are then used as enter to a fuzzy inference gadget, which tactics the enter values using fuzzy good judgment operators and linguistic policies to provide crisp, numerical output values that define the class label of every pixel. by way of enforcing fuzzy good judgment primarily based strategies for HSI category, the difficulty of high complexity may be addressed as the unambiguous output of the bushy common sense gadget simplifies the information evaluation mission.
{"title":"Unsupervised Classification of Hyper Spectral Images using Feature Extraction and Fuzzy Logic","authors":"A. Kannagi, Chetan Chaudhary, Jyoti Seth","doi":"10.1109/ICOCWC60930.2024.10470557","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470557","url":null,"abstract":"Hyperspectral pictures are complicated information items with high spectral resolution, making their categorization and analysis timeingesting and challenging. Traditional strategies for classifying hyperspectral pix may be unreliable and gradually attributable to the presence of diverse noise resources and a high range of pixels. This paper proposes a new unsupervised classification approach for hyperspectral pictures using function extraction and fuzzy common sense. The method starts by first using feature extraction techniques on the hyperspectral pictures to lessen the dimensionality of the facts. Numerous characteristic extraction algorithms, including primary thing analysis (PCA) and impartial component evaluation (ICA), are tested to determine which function extraction algorithms yield satisfactory effects. The reduced function area is then used as an entry for the fuzzy category system. The bushy common sense device is used to classify the hyperspectral pix into distinctive classes according to the extracted capabilities. Experimental results display that the proposed method achieves proper effects for the category venture with classification accuracy accomplishing as high as 79%. The proposed technique demonstrates advanced performance over conventional category strategies in terms of each accuracy and speed. Hyperspectral pics (HSI) offer valuable statistics approximately the environment and the functions gift inside it. But, the sheer quantity of facts present in HSI makes guide evaluation of those photos a time-eating and exhausting project. As such, there is a growing demand for robust and reliable automated techniques to analyze HSI. In this context, unsupervised tactics for classifying HSI have gained interest due to their ability to examine facts without requiring manually categorized education facts. Fuzzy logic is one method being explored for unsupervised HSI type due to its capability to assign more than one label to pixels of the image and its robustness to noise. Right here, the HSI picture is first pre-processed and feature extracted to produce a fixed of numerical statistics that may be used to classify the pixels of the image extra as they should be. This feature extracted records are then used as enter to a fuzzy inference gadget, which tactics the enter values using fuzzy good judgment operators and linguistic policies to provide crisp, numerical output values that define the class label of every pixel. by way of enforcing fuzzy good judgment primarily based strategies for HSI category, the difficulty of high complexity may be addressed as the unambiguous output of the bushy common sense gadget simplifies the information evaluation mission.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"44 3","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470695
D. Yadav, Neeraj Kumari, Syed Harron
Convolutional neural networks (CNNs) have emerged as a powerful tool for object detection and recognition. Recent advances in CNNs have improved their performance on object detection by incorporating innovative convolutional layers and architectures. These advances include the inception architecture, region proposal networks (RPNs), and fully convolutional networks (FCNs). Additionally, these architectures have enabled object detection and recognition with significant improvements in accuracy and speed. Furthermore, recent research has focused on applying deep transfer learning techniques to CNNs for object detection and recognition, which have shown promising results in terms of precision and accuracy. Overall, these ongoing advancements have further improved the state of the art in object detection and recognition tasks.
{"title":"Advances in Convolutional Neural Networks for Object Detection and Recognition","authors":"D. Yadav, Neeraj Kumari, Syed Harron","doi":"10.1109/ICOCWC60930.2024.10470695","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470695","url":null,"abstract":"Convolutional neural networks (CNNs) have emerged as a powerful tool for object detection and recognition. Recent advances in CNNs have improved their performance on object detection by incorporating innovative convolutional layers and architectures. These advances include the inception architecture, region proposal networks (RPNs), and fully convolutional networks (FCNs). Additionally, these architectures have enabled object detection and recognition with significant improvements in accuracy and speed. Furthermore, recent research has focused on applying deep transfer learning techniques to CNNs for object detection and recognition, which have shown promising results in terms of precision and accuracy. Overall, these ongoing advancements have further improved the state of the art in object detection and recognition tasks.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"92 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470646
Dharman J, H. Patil, B. S. Halakanimath
Optical amplifiers play a vital position in lengthy-haul transmission systems through boosting the signal energy in fiber optic verbal exchange networks. However, the performance of those amplifiers may be appreciably impacted by using strength transients that are sudden fluctuations in the strength level of the signal. This could lead to distorted signals and affect the overall performance of the entire transmission device. Strength transients can arise due to various factors, along with temperature modifications, fiber losses, or fluctuations in the input strength. Those transients can motivate the optical amplifier to perform in non-linear locations, resulting in sign distortion and degradation. This could cause mistakes inside the transmission, reducing the facts transmission rate and increasing the bit errors rate. It's vital to recognize the effect of energy transients to properly characterize and examine the performance of optical amplifiers. This is critical in the design and optimization of lengthy-haul transmission structures and the improvement of effective reimbursement strategies.
{"title":"Impact of Power Transients on Optical Amplifier Characterization and Performance in Long-Haul Transmission Systems","authors":"Dharman J, H. Patil, B. S. Halakanimath","doi":"10.1109/ICOCWC60930.2024.10470646","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470646","url":null,"abstract":"Optical amplifiers play a vital position in lengthy-haul transmission systems through boosting the signal energy in fiber optic verbal exchange networks. However, the performance of those amplifiers may be appreciably impacted by using strength transients that are sudden fluctuations in the strength level of the signal. This could lead to distorted signals and affect the overall performance of the entire transmission device. Strength transients can arise due to various factors, along with temperature modifications, fiber losses, or fluctuations in the input strength. Those transients can motivate the optical amplifier to perform in non-linear locations, resulting in sign distortion and degradation. This could cause mistakes inside the transmission, reducing the facts transmission rate and increasing the bit errors rate. It's vital to recognize the effect of energy transients to properly characterize and examine the performance of optical amplifiers. This is critical in the design and optimization of lengthy-haul transmission structures and the improvement of effective reimbursement strategies.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"72 43","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470855
Haripriya, Neeraj Das, Prashant Kumar
This paper examines novel techniques for interactive communication media algorithms for networking applications. In latest a long time, there have been substantial traits within the field of statistics communications. As a result, various alternatives for interactive communique media algorithms gift themselves. This paper investigates the usage of various media algorithms for networking packages. Those algorithms consist of peer-to-peer networking, distributed walking, grid computing, and ant colony optimization. A comparative evaluation of these media algorithms is provided via an assessment of the literature and a compilation of to-be-had assets. The overall performance metrics and features of interactive conversation media algorithms are discussed, and their effectiveness and performance are compared. The research also examines the present-day kingdom of interactive media algorithms and their future possibilities. Consequently, a conclusion is drawn regarding the suitability of different algorithms for networked programs. The technical abstract describes the research investigating novel techniques for interactive conversation media algorithms for networking applications. The research aims to provide solutions to expanding allotted communique media algorithms in a dispensed networking device. The specific research focuses on growing advanced algorithms for interactive communication media to aid remote communications on networks. The studies will emphasize numerous robust implementations of the algorithms and the use of mathematical modeling and simulations. The research intends to analyze the proposed algorithms' performance and scalability in numerous networking environments. Moreover, the studies will look at the software of algorithms for numerous Wi-Fi verbal exchange protocols consisting of the IEEE 802.11n general. In the end, the studies may also explore the security factors of the proposed algorithms, with a particular interest in the range of algorithms used in the Wi-Fi conversation protocols. The consequences of this research will provide the perception of the present-day and potential destiny applications of conversation media algorithms in networking environments. Consequently, this study will shortly enhance the opportunities available for software developers and network gadget carriers..
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